Overview

Dataset statistics

Number of variables18
Number of observations186476
Missing cells15
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.8 MiB
Average record size in memory224.0 B

Variable types

Text2
Categorical10
Numeric6

Alerts

State has constant value "WA"Constant
City has a high cardinality: 473 distinct valuesHigh cardinality
Model has a high cardinality: 143 distinct valuesHigh cardinality
Electric Utility has a high cardinality: 75 distinct valuesHigh cardinality
2020 Census Tract has a high cardinality: 1768 distinct valuesHigh cardinality
Electric Utility is highly imbalanced (51.8%)Imbalance
DOL Vehicle ID has unique valuesUnique
Electric Range has 98599 (52.9%) zerosZeros

Reproduction

Analysis started2024-06-05 22:02:51.139244
Analysis finished2024-06-05 22:03:01.062892
Duration9.92 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct11237
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2024-06-05T18:03:01.445311image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1864760
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2077 ?
Unique (%)1.1%

Sample

1st rowWBY8P6C58K
2nd row5YJSA1DN4D
3rd row5YJSA1E26J
4th rowWBY2Z2C54E
5th row5YJXCDE23J
ValueCountFrequency (%)
7saygdee6p 1236
 
0.7%
7saygdee7p 1227
 
0.7%
7saygdee5p 1192
 
0.6%
7saygdee8p 1190
 
0.6%
7saygdeexp 1189
 
0.6%
7saygdee0p 1168
 
0.6%
7saygdee9p 1161
 
0.6%
7saygdee2p 1157
 
0.6%
7saygdee3p 1148
 
0.6%
7saygdee4p 1128
 
0.6%
Other values (11227) 174680
93.7%
2024-06-05T18:03:02.323899image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 168216
 
9.0%
1 131918
 
7.1%
A 118815
 
6.4%
Y 104715
 
5.6%
P 92679
 
5.0%
J 90577
 
4.9%
5 85545
 
4.6%
3 75741
 
4.1%
D 74092
 
4.0%
G 72671
 
3.9%
Other values (24) 849791
45.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1864760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 168216
 
9.0%
1 131918
 
7.1%
A 118815
 
6.4%
Y 104715
 
5.6%
P 92679
 
5.0%
J 90577
 
4.9%
5 85545
 
4.6%
3 75741
 
4.1%
D 74092
 
4.0%
G 72671
 
3.9%
Other values (24) 849791
45.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1864760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 168216
 
9.0%
1 131918
 
7.1%
A 118815
 
6.4%
Y 104715
 
5.6%
P 92679
 
5.0%
J 90577
 
4.9%
5 85545
 
4.6%
3 75741
 
4.1%
D 74092
 
4.0%
G 72671
 
3.9%
Other values (24) 849791
45.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1864760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 168216
 
9.0%
1 131918
 
7.1%
A 118815
 
6.4%
Y 104715
 
5.6%
P 92679
 
5.0%
J 90577
 
4.9%
5 85545
 
4.6%
3 75741
 
4.1%
D 74092
 
4.0%
G 72671
 
3.9%
Other values (24) 849791
45.6%

County
Categorical

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
King
97012 
Snohomish
22086 
Pierce
14542 
Clark
11005 
Thurston
 
6779
Other values (34)
35052 

Length

Max length12
Median length4
Mean length5.4712456
Min length4

Characters and Unicode

Total characters1020256
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKing
2nd rowKitsap
3rd rowKing
4th rowKing
5th rowKing

Common Values

ValueCountFrequency (%)
King 97012
52.0%
Snohomish 22086
 
11.8%
Pierce 14542
 
7.8%
Clark 11005
 
5.9%
Thurston 6779
 
3.6%
Kitsap 6145
 
3.3%
Spokane 4856
 
2.6%
Whatcom 4455
 
2.4%
Benton 2282
 
1.2%
Skagit 2028
 
1.1%
Other values (29) 15286
 
8.2%

Length

2024-06-05T18:03:02.807594image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
king 97012
51.4%
snohomish 22086
 
11.7%
pierce 14542
 
7.7%
clark 11005
 
5.8%
thurston 6779
 
3.6%
kitsap 6145
 
3.3%
spokane 4856
 
2.6%
whatcom 4455
 
2.4%
benton 2282
 
1.2%
skagit 2028
 
1.1%
Other values (32) 17439
 
9.2%

Most occurring characters

ValueCountFrequency (%)
i 148482
14.6%
n 145665
14.3%
K 104132
10.2%
g 99697
9.8%
o 66880
 
6.6%
h 56946
 
5.6%
a 46429
 
4.6%
s 41798
 
4.1%
e 40906
 
4.0%
r 36743
 
3.6%
Other values (32) 232578
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1020256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 148482
14.6%
n 145665
14.3%
K 104132
10.2%
g 99697
9.8%
o 66880
 
6.6%
h 56946
 
5.6%
a 46429
 
4.6%
s 41798
 
4.1%
e 40906
 
4.0%
r 36743
 
3.6%
Other values (32) 232578
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1020256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 148482
14.6%
n 145665
14.3%
K 104132
10.2%
g 99697
9.8%
o 66880
 
6.6%
h 56946
 
5.6%
a 46429
 
4.6%
s 41798
 
4.1%
e 40906
 
4.0%
r 36743
 
3.6%
Other values (32) 232578
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1020256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 148482
14.6%
n 145665
14.3%
K 104132
10.2%
g 99697
9.8%
o 66880
 
6.6%
h 56946
 
5.6%
a 46429
 
4.6%
s 41798
 
4.1%
e 40906
 
4.0%
r 36743
 
3.6%
Other values (32) 232578
22.8%

City
Categorical

HIGH CARDINALITY 

Distinct473
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Seattle
30873 
Bellevue
 
9370
Redmond
 
6747
Vancouver
 
6531
Bothell
 
6151
Other values (468)
126804 

Length

Max length24
Median length17
Mean length8.2062839
Min length3

Characters and Unicode

Total characters1530275
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)< 0.1%

Sample

1st rowSeattle
2nd rowBremerton
3rd rowKent
4th rowBellevue
5th rowBellevue

Common Values

ValueCountFrequency (%)
Seattle 30873
 
16.6%
Bellevue 9370
 
5.0%
Redmond 6747
 
3.6%
Vancouver 6531
 
3.5%
Bothell 6151
 
3.3%
Kirkland 5608
 
3.0%
Sammamish 5494
 
2.9%
Renton 5399
 
2.9%
Olympia 4501
 
2.4%
Tacoma 3891
 
2.1%
Other values (463) 101911
54.7%

Length

2024-06-05T18:03:03.059087image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seattle 30873
 
14.3%
bellevue 9370
 
4.3%
redmond 6747
 
3.1%
vancouver 6531
 
3.0%
bothell 6151
 
2.8%
kirkland 5608
 
2.6%
sammamish 5494
 
2.5%
renton 5399
 
2.5%
island 5191
 
2.4%
olympia 4501
 
2.1%
Other values (493) 130331
60.3%

Most occurring characters

ValueCountFrequency (%)
e 208868
13.6%
a 148439
 
9.7%
l 135925
 
8.9%
t 106375
 
7.0%
n 101541
 
6.6%
o 90253
 
5.9%
r 63336
 
4.1%
i 60727
 
4.0%
S 52499
 
3.4%
d 50920
 
3.3%
Other values (42) 511392
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1530275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 208868
13.6%
a 148439
 
9.7%
l 135925
 
8.9%
t 106375
 
7.0%
n 101541
 
6.6%
o 90253
 
5.9%
r 63336
 
4.1%
i 60727
 
4.0%
S 52499
 
3.4%
d 50920
 
3.3%
Other values (42) 511392
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1530275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 208868
13.6%
a 148439
 
9.7%
l 135925
 
8.9%
t 106375
 
7.0%
n 101541
 
6.6%
o 90253
 
5.9%
r 63336
 
4.1%
i 60727
 
4.0%
S 52499
 
3.4%
d 50920
 
3.3%
Other values (42) 511392
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1530275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 208868
13.6%
a 148439
 
9.7%
l 135925
 
8.9%
t 106375
 
7.0%
n 101541
 
6.6%
o 90253
 
5.9%
r 63336
 
4.1%
i 60727
 
4.0%
S 52499
 
3.4%
d 50920
 
3.3%
Other values (42) 511392
33.4%

State
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
WA
186476 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters372952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 186476
100.0%

Length

2024-06-05T18:03:03.204675image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:03:03.661071image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
ValueCountFrequency (%)
wa 186476
100.0%

Most occurring characters

ValueCountFrequency (%)
W 186476
50.0%
A 186476
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 186476
50.0%
A 186476
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 186476
50.0%
A 186476
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 186476
50.0%
A 186476
50.0%

Postal Code
Real number (ℝ)

Distinct548
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98261.658
Minimum98001
Maximum99403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T18:03:03.797076image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98007
Q198052
median98122
Q398371
95-th percentile98944
Maximum99403
Range1402
Interquartile range (IQR)319

Descriptive statistics

Standard deviation304.62624
Coefficient of variation (CV)0.0031001537
Kurtosis3.0048493
Mean98261.658
Median Absolute Deviation (MAD)100
Skewness1.8046731
Sum1.8323441 × 1010
Variance92797.148
MonotonicityNot monotonic
2024-06-05T18:03:04.027291image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 4753
 
2.5%
98012 3507
 
1.9%
98033 3197
 
1.7%
98188 3063
 
1.6%
98006 3004
 
1.6%
98004 2937
 
1.6%
98115 2833
 
1.5%
98074 2633
 
1.4%
98072 2545
 
1.4%
98034 2468
 
1.3%
Other values (538) 155536
83.4%
ValueCountFrequency (%)
98001 786
 
0.4%
98002 284
 
0.2%
98003 569
 
0.3%
98004 2937
1.6%
98005 1368
0.7%
98006 3004
1.6%
98007 996
 
0.5%
98008 1550
0.8%
98010 390
 
0.2%
98011 1209
0.6%
ValueCountFrequency (%)
99403 63
 
< 0.1%
99402 12
 
< 0.1%
99371 1
 
< 0.1%
99362 361
0.2%
99361 10
 
< 0.1%
99360 8
 
< 0.1%
99357 22
 
< 0.1%
99356 1
 
< 0.1%
99354 301
0.2%
99353 237
0.1%

Model Year
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.6611
Minimum1997
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T18:03:04.174282image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2014
Q12019
median2022
Q32023
95-th percentile2024
Maximum2024
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9913893
Coefficient of variation (CV)0.0014804013
Kurtosis0.68764134
Mean2020.6611
Median Absolute Deviation (MAD)1
Skewness-1.1612314
Sum3.768048 × 108
Variance8.9484102
MonotonicityNot monotonic
2024-06-05T18:03:04.305531image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2023 59230
31.8%
2022 28067
15.1%
2021 19254
 
10.3%
2018 14344
 
7.7%
2024 13451
 
7.2%
2020 11990
 
6.4%
2019 10904
 
5.8%
2017 8605
 
4.6%
2016 5521
 
3.0%
2015 4830
 
2.6%
Other values (12) 10280
 
5.5%
ValueCountFrequency (%)
1997 1
 
< 0.1%
1998 1
 
< 0.1%
1999 5
 
< 0.1%
2000 7
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 20
 
< 0.1%
2010 24
 
< 0.1%
2011 757
0.4%
2012 1589
0.9%
ValueCountFrequency (%)
2024 13451
 
7.2%
2023 59230
31.8%
2022 28067
15.1%
2021 19254
 
10.3%
2020 11990
 
6.4%
2019 10904
 
5.8%
2018 14344
 
7.7%
2017 8605
 
4.6%
2016 5521
 
3.0%
2015 4830
 
2.6%

Make
Categorical

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
TESLA
83149 
NISSAN
14168 
CHEVROLET
14046 
FORD
9812 
KIA
 
7875
Other values (35)
57426 

Length

Max length20
Median length14
Mean length5.5569832
Min length3

Characters and Unicode

Total characters1036244
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBMW
2nd rowTESLA
3rd rowTESLA
4th rowBMW
5th rowTESLA

Common Values

ValueCountFrequency (%)
TESLA 83149
44.6%
NISSAN 14168
 
7.6%
CHEVROLET 14046
 
7.5%
FORD 9812
 
5.3%
KIA 7875
 
4.2%
BMW 7843
 
4.2%
TOYOTA 6735
 
3.6%
VOLKSWAGEN 5286
 
2.8%
JEEP 4915
 
2.6%
HYUNDAI 4776
 
2.6%
Other values (30) 27871
 
14.9%

Length

2024-06-05T18:03:04.509601image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 83149
44.6%
nissan 14168
 
7.6%
chevrolet 14046
 
7.5%
ford 9812
 
5.3%
kia 7875
 
4.2%
bmw 7843
 
4.2%
toyota 6735
 
3.6%
volkswagen 5286
 
2.8%
jeep 4915
 
2.6%
hyundai 4776
 
2.6%
Other values (36) 27990
 
15.0%

Most occurring characters

ValueCountFrequency (%)
E 140442
13.6%
A 136672
13.2%
S 128015
12.4%
T 113607
11.0%
L 113428
10.9%
O 55352
 
5.3%
N 47275
 
4.6%
I 46717
 
4.5%
R 40401
 
3.9%
V 32716
 
3.2%
Other values (18) 181619
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1036244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 140442
13.6%
A 136672
13.2%
S 128015
12.4%
T 113607
11.0%
L 113428
10.9%
O 55352
 
5.3%
N 47275
 
4.6%
I 46717
 
4.5%
R 40401
 
3.9%
V 32716
 
3.2%
Other values (18) 181619
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1036244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 140442
13.6%
A 136672
13.2%
S 128015
12.4%
T 113607
11.0%
L 113428
10.9%
O 55352
 
5.3%
N 47275
 
4.6%
I 46717
 
4.5%
R 40401
 
3.9%
V 32716
 
3.2%
Other values (18) 181619
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1036244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 140442
13.6%
A 136672
13.2%
S 128015
12.4%
T 113607
11.0%
L 113428
10.9%
O 55352
 
5.3%
N 47275
 
4.6%
I 46717
 
4.5%
R 40401
 
3.9%
V 32716
 
3.2%
Other values (18) 181619
17.5%

Model
Categorical

HIGH CARDINALITY 

Distinct143
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
MODEL Y
38577 
MODEL 3
30619 
LEAF
13422 
MODEL S
 
7809
BOLT EV
 
7045
Other values (138)
89004 

Length

Max length24
Median length7
Mean length6.4071516
Min length2

Characters and Unicode

Total characters1194780
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowI3
2nd rowMODEL S
3rd rowMODEL S
4th rowI8
5th rowMODEL X

Common Values

ValueCountFrequency (%)
MODEL Y 38577
20.7%
MODEL 3 30619
16.4%
LEAF 13422
 
7.2%
MODEL S 7809
 
4.2%
BOLT EV 7045
 
3.8%
MODEL X 6031
 
3.2%
VOLT 4795
 
2.6%
ID.4 4211
 
2.3%
WRANGLER 3691
 
2.0%
MUSTANG MACH-E 3630
 
1.9%
Other values (133) 66646
35.7%

Length

2024-06-05T18:03:04.708482image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
model 83036
28.0%
y 38577
13.0%
3 30619
 
10.3%
leaf 13422
 
4.5%
bolt 8916
 
3.0%
s 7809
 
2.6%
ev 7350
 
2.5%
x 6031
 
2.0%
prime 5357
 
1.8%
volt 4795
 
1.6%
Other values (140) 90473
30.5%

Most occurring characters

ValueCountFrequency (%)
E 140078
11.7%
L 122659
 
10.3%
O 117680
 
9.8%
109909
 
9.2%
M 97701
 
8.2%
D 91230
 
7.6%
A 50742
 
4.2%
Y 41873
 
3.5%
R 41549
 
3.5%
I 41078
 
3.4%
Other values (28) 340281
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1194780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 140078
11.7%
L 122659
 
10.3%
O 117680
 
9.8%
109909
 
9.2%
M 97701
 
8.2%
D 91230
 
7.6%
A 50742
 
4.2%
Y 41873
 
3.5%
R 41549
 
3.5%
I 41078
 
3.4%
Other values (28) 340281
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1194780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 140078
11.7%
L 122659
 
10.3%
O 117680
 
9.8%
109909
 
9.2%
M 97701
 
8.2%
D 91230
 
7.6%
A 50742
 
4.2%
Y 41873
 
3.5%
R 41549
 
3.5%
I 41078
 
3.4%
Other values (28) 340281
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1194780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 140078
11.7%
L 122659
 
10.3%
O 117680
 
9.8%
109909
 
9.2%
M 97701
 
8.2%
D 91230
 
7.6%
A 50742
 
4.2%
Y 41873
 
3.5%
R 41549
 
3.5%
I 41078
 
3.4%
Other values (28) 340281
28.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Battery Electric Vehicle (BEV)
146023 
Plug-in Hybrid Electric Vehicle (PHEV)
40453 

Length

Max length38
Median length30
Mean length31.735473
Min length30

Characters and Unicode

Total characters5917904
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBattery Electric Vehicle (BEV)
2nd rowBattery Electric Vehicle (BEV)
3rd rowBattery Electric Vehicle (BEV)
4th rowPlug-in Hybrid Electric Vehicle (PHEV)
5th rowBattery Electric Vehicle (BEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 146023
78.3%
Plug-in Hybrid Electric Vehicle (PHEV) 40453
 
21.7%

Length

2024-06-05T18:03:04.890874image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:03:05.040797image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
ValueCountFrequency (%)
electric 186476
23.7%
vehicle 186476
23.7%
battery 146023
18.6%
bev 146023
18.6%
plug-in 40453
 
5.1%
hybrid 40453
 
5.1%
phev 40453
 
5.1%

Most occurring characters

ValueCountFrequency (%)
e 705451
11.9%
599881
10.1%
c 559428
9.5%
t 478522
 
8.1%
i 453858
 
7.7%
l 413405
 
7.0%
V 372952
 
6.3%
r 372952
 
6.3%
E 372952
 
6.3%
B 292046
 
4.9%
Other values (13) 1296457
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5917904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 705451
11.9%
599881
10.1%
c 559428
9.5%
t 478522
 
8.1%
i 453858
 
7.7%
l 413405
 
7.0%
V 372952
 
6.3%
r 372952
 
6.3%
E 372952
 
6.3%
B 292046
 
4.9%
Other values (13) 1296457
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5917904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 705451
11.9%
599881
10.1%
c 559428
9.5%
t 478522
 
8.1%
i 453858
 
7.7%
l 413405
 
7.0%
V 372952
 
6.3%
r 372952
 
6.3%
E 372952
 
6.3%
B 292046
 
4.9%
Other values (13) 1296457
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5917904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 705451
11.9%
599881
10.1%
c 559428
9.5%
t 478522
 
8.1%
i 453858
 
7.7%
l 413405
 
7.0%
V 372952
 
6.3%
r 372952
 
6.3%
E 372952
 
6.3%
B 292046
 
4.9%
Other values (13) 1296457
21.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Eligibility unknown as battery range has not been researched
98599 
Clean Alternative Fuel Vehicle Eligible
67637 
Not eligible due to low battery range
20240 

Length

Max length60
Median length60
Mean length49.88665
Min length37

Characters and Unicode

Total characters9302663
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClean Alternative Fuel Vehicle Eligible
2nd rowClean Alternative Fuel Vehicle Eligible
3rd rowClean Alternative Fuel Vehicle Eligible
4th rowNot eligible due to low battery range
5th rowClean Alternative Fuel Vehicle Eligible

Common Values

ValueCountFrequency (%)
Eligibility unknown as battery range has not been researched 98599
52.9%
Clean Alternative Fuel Vehicle Eligible 67637
36.3%
Not eligible due to low battery range 20240
 
10.9%

Length

2024-06-05T18:03:05.204503image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T18:03:05.357916image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
ValueCountFrequency (%)
not 118839
 
8.7%
battery 118839
 
8.7%
range 118839
 
8.7%
eligibility 98599
 
7.2%
unknown 98599
 
7.2%
been 98599
 
7.2%
researched 98599
 
7.2%
has 98599
 
7.2%
as 98599
 
7.2%
eligible 87877
 
6.4%
Other values (7) 331268
24.2%

Most occurring characters

ValueCountFrequency (%)
e 1264852
13.6%
1180780
12.7%
n 747108
 
8.0%
i 705424
 
7.6%
a 668749
 
7.2%
l 663740
 
7.1%
t 610630
 
6.6%
r 502513
 
5.4%
b 403914
 
4.3%
g 305315
 
3.3%
Other values (16) 2249638
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9302663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1264852
13.6%
1180780
12.7%
n 747108
 
8.0%
i 705424
 
7.6%
a 668749
 
7.2%
l 663740
 
7.1%
t 610630
 
6.6%
r 502513
 
5.4%
b 403914
 
4.3%
g 305315
 
3.3%
Other values (16) 2249638
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9302663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1264852
13.6%
1180780
12.7%
n 747108
 
8.0%
i 705424
 
7.6%
a 668749
 
7.2%
l 663740
 
7.1%
t 610630
 
6.6%
r 502513
 
5.4%
b 403914
 
4.3%
g 305315
 
3.3%
Other values (16) 2249638
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9302663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1264852
13.6%
1180780
12.7%
n 747108
 
8.0%
i 705424
 
7.6%
a 668749
 
7.2%
l 663740
 
7.1%
t 610630
 
6.6%
r 502513
 
5.4%
b 403914
 
4.3%
g 305315
 
3.3%
Other values (16) 2249638
24.2%

Electric Range
Real number (ℝ)

ZEROS 

Distinct103
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.683101
Minimum0
Maximum337
Zeros98599
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T18:03:05.539263image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q373
95-th percentile259
Maximum337
Range337
Interquartile range (IQR)73

Descriptive statistics

Standard deviation90.770218
Coefficient of variation (CV)1.6013629
Kurtosis0.79635721
Mean56.683101
Median Absolute Deviation (MAD)0
Skewness1.5095775
Sum10570038
Variance8239.2324
MonotonicityNot monotonic
2024-06-05T18:03:05.776624image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 98599
52.9%
215 6420
 
3.4%
32 4321
 
2.3%
25 4253
 
2.3%
220 4043
 
2.2%
238 3941
 
2.1%
84 3893
 
2.1%
21 3839
 
2.1%
38 2491
 
1.3%
19 2474
 
1.3%
Other values (93) 52202
28.0%
ValueCountFrequency (%)
0 98599
52.9%
6 936
 
0.5%
8 37
 
< 0.1%
9 20
 
< 0.1%
10 170
 
0.1%
11 3
 
< 0.1%
12 169
 
0.1%
13 362
 
0.2%
14 1106
 
0.6%
15 87
 
< 0.1%
ValueCountFrequency (%)
337 77
 
< 0.1%
330 332
 
0.2%
322 1700
0.9%
308 513
 
0.3%
293 448
 
0.2%
291 2391
1.3%
289 659
 
0.4%
270 272
 
0.1%
266 1441
0.8%
265 129
 
0.1%
Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
41.0
 
12066
45.0
 
11201
48.0
 
10246
1.0
 
8141
11.0
 
8072
Other values (44)
136750 

Length

Max length4
Median length4
Mean length3.858223
Min length3

Characters and Unicode

Total characters719466
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43.0
2nd row35.0
3rd row47.0
4th row41.0
5th row41.0

Common Values

ValueCountFrequency (%)
41.0 12066
 
6.5%
45.0 11201
 
6.0%
48.0 10246
 
5.5%
1.0 8141
 
4.4%
11.0 8072
 
4.3%
5.0 7975
 
4.3%
36.0 7734
 
4.1%
46.0 7175
 
3.8%
43.0 6806
 
3.6%
37.0 5466
 
2.9%
Other values (39) 101594
54.5%

Length

2024-06-05T18:03:05.931648image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41.0 12066
 
6.5%
45.0 11201
 
6.0%
48.0 10246
 
5.5%
1.0 8141
 
4.4%
11.0 8072
 
4.3%
5.0 7975
 
4.3%
36.0 7734
 
4.1%
46.0 7175
 
3.8%
43.0 6806
 
3.6%
37.0 5466
 
2.9%
Other values (39) 101594
54.5%

Most occurring characters

ValueCountFrequency (%)
0 197586
27.5%
. 186476
25.9%
4 80662
11.2%
1 62333
 
8.7%
3 56006
 
7.8%
2 45130
 
6.3%
5 24232
 
3.4%
8 21459
 
3.0%
6 21377
 
3.0%
7 15518
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 719466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 197586
27.5%
. 186476
25.9%
4 80662
11.2%
1 62333
 
8.7%
3 56006
 
7.8%
2 45130
 
6.3%
5 24232
 
3.4%
8 21459
 
3.0%
6 21377
 
3.0%
7 15518
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 719466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 197586
27.5%
. 186476
25.9%
4 80662
11.2%
1 62333
 
8.7%
3 56006
 
7.8%
2 45130
 
6.3%
5 24232
 
3.4%
8 21459
 
3.0%
6 21377
 
3.0%
7 15518
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 719466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 197586
27.5%
. 186476
25.9%
4 80662
11.2%
1 62333
 
8.7%
3 56006
 
7.8%
2 45130
 
6.3%
5 24232
 
3.4%
8 21459
 
3.0%
6 21377
 
3.0%
7 15518
 
2.2%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct186476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2258488 × 108
Minimum4385
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T18:03:06.071821image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile1.1150147 × 108
Q11.8515839 × 108
median2.302291 × 108
Q32.578034 × 108
95-th percentile3.3234198 × 108
Maximum4.7925477 × 108
Range4.7925039 × 108
Interquartile range (IQR)72645006

Descriptive statistics

Standard deviation74638849
Coefficient of variation (CV)0.33532758
Kurtosis3.5760834
Mean2.2258488 × 108
Median Absolute Deviation (MAD)30487219
Skewness0.519861
Sum4.1506738 × 1013
Variance5.5709578 × 1015
MonotonicityNot monotonic
2024-06-05T18:03:06.234886image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
259254397 1
 
< 0.1%
253350730 1
 
< 0.1%
143294280 1
 
< 0.1%
168433310 1
 
< 0.1%
185375284 1
 
< 0.1%
311629209 1
 
< 0.1%
294923555 1
 
< 0.1%
260781657 1
 
< 0.1%
122165865 1
 
< 0.1%
206956011 1
 
< 0.1%
Other values (186466) 186466
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
61092 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
Distinct547
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Memory size16.9 MiB
2024-06-05T18:03:06.616764image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length33
Median length31
Mean length30.091156
Min length26

Characters and Unicode

Total characters5611128
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowPOINT (-122.3008235 47.6862671)
2nd rowPOINT (-122.6961203 47.5759584)
3rd rowPOINT (-122.1145138 47.3581107)
4th rowPOINT (-122.202397 47.619252)
5th rowPOINT (-122.202397 47.619252)
ValueCountFrequency (%)
point 186471
33.3%
47.6705374 4753
 
0.8%
122.1207376 4753
 
0.8%
122.206146 3507
 
0.6%
47.839957 3507
 
0.6%
122.1925969 3197
 
0.6%
47.676241 3197
 
0.6%
122.271716 3063
 
0.5%
47.452837 3063
 
0.5%
122.144149 3004
 
0.5%
Other values (1085) 340898
60.9%
2024-06-05T18:03:07.209017image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 607884
 
10.8%
1 447679
 
8.0%
4 423651
 
7.6%
7 400974
 
7.1%
. 372942
 
6.6%
372942
 
6.6%
6 293302
 
5.2%
3 277486
 
4.9%
5 254869
 
4.5%
8 230059
 
4.1%
Other values (10) 1929340
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5611128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 607884
 
10.8%
1 447679
 
8.0%
4 423651
 
7.6%
7 400974
 
7.1%
. 372942
 
6.6%
372942
 
6.6%
6 293302
 
5.2%
3 277486
 
4.9%
5 254869
 
4.5%
8 230059
 
4.1%
Other values (10) 1929340
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5611128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 607884
 
10.8%
1 447679
 
8.0%
4 423651
 
7.6%
7 400974
 
7.1%
. 372942
 
6.6%
372942
 
6.6%
6 293302
 
5.2%
3 277486
 
4.9%
5 254869
 
4.5%
8 230059
 
4.1%
Other values (10) 1929340
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5611128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 607884
 
10.8%
1 447679
 
8.0%
4 423651
 
7.6%
7 400974
 
7.1%
. 372942
 
6.6%
372942
 
6.6%
6 293302
 
5.2%
3 277486
 
4.9%
5 254869
 
4.5%
8 230059
 
4.1%
Other values (10) 1929340
34.4%

Latitude
Real number (ℝ)

Distinct547
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean47.462836
Minimum45.595997
Maximum48.992052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T18:03:07.440636image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum45.595997
5-th percentile45.74169
Q147.358111
median47.610347
Q347.721052
95-th percentile48.198211
Maximum48.992052
Range3.3960551
Interquartile range (IQR)0.3629411

Descriptive statistics

Standard deviation0.61088183
Coefficient of variation (CV)0.01287074
Kurtosis2.8485739
Mean47.462836
Median Absolute Deviation (MAD)0.1575102
Skewness-1.3787686
Sum8850442.5
Variance0.37317661
MonotonicityNot monotonic
2024-06-05T18:03:07.665298image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.6705374 4753
 
2.5%
47.839957 3507
 
1.9%
47.676241 3197
 
1.7%
47.452837 3063
 
1.6%
47.560742 3004
 
1.6%
47.619252 2937
 
1.6%
47.6862671 2833
 
1.5%
47.6259676 2633
 
1.4%
47.7621254 2545
 
1.4%
47.7210518 2468
 
1.3%
Other values (537) 155531
83.4%
ValueCountFrequency (%)
45.595997 609
 
0.3%
45.604689 755
0.4%
45.6092439 1692
0.9%
45.6174915 9
 
< 0.1%
45.620105 406
 
0.2%
45.6278995 560
 
0.3%
45.6365338 327
 
0.2%
45.6397193 11
 
< 0.1%
45.6456491 641
 
0.3%
45.654422 255
 
0.1%
ValueCountFrequency (%)
48.9920521 1
 
< 0.1%
48.9910612 58
 
< 0.1%
48.9769756 14
 
< 0.1%
48.953176 412
0.2%
48.952558 28
 
< 0.1%
48.9425893 234
0.1%
48.9395869 31
 
< 0.1%
48.9335881 20
 
< 0.1%
48.9260491 11
 
< 0.1%
48.9119603 2
 
< 0.1%

Longitude
Real number (ℝ)

Distinct547
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-122.07943
Minimum-124.61408
Maximum-117.05952
Zeros0
Zeros (%)0.0%
Negative186471
Negative (%)> 99.9%
Memory size2.8 MiB
2024-06-05T18:03:08.076035image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum-124.61408
5-th percentile-122.86491
Q1-122.39552
median-122.27533
Q3-122.1368
95-th percentile-119.36274
Maximum-117.05952
Range7.5545593
Interquartile range (IQR)0.2587159

Descriptive statistics

Standard deviation1.0205948
Coefficient of variation (CV)-0.0083600882
Kurtosis12.695332
Mean-122.07943
Median Absolute Deviation (MAD)0.1316586
Skewness3.512928
Sum-22764273
Variance1.0416137
MonotonicityNot monotonic
2024-06-05T18:03:08.335539image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.1207376 4753
 
2.5%
-122.206146 3507
 
1.9%
-122.1925969 3197
 
1.7%
-122.271716 3063
 
1.6%
-122.144149 3004
 
1.6%
-122.202397 2937
 
1.6%
-122.3008235 2833
 
1.5%
-122.0430602 2633
 
1.4%
-122.1368031 2545
 
1.4%
-122.2026532 2468
 
1.3%
Other values (537) 155531
83.4%
ValueCountFrequency (%)
-124.6140781 7
 
< 0.1%
-124.4071496 14
 
< 0.1%
-124.3746458 3
 
< 0.1%
-124.3405386 2
 
< 0.1%
-124.2846109 4
 
< 0.1%
-124.264464 2
 
< 0.1%
-124.2129105 4
 
< 0.1%
-124.1836215 26
 
< 0.1%
-124.1682283 4
 
< 0.1%
-124.154452 140
0.1%
ValueCountFrequency (%)
-117.0595188 12
 
< 0.1%
-117.075672 48
 
< 0.1%
-117.0774369 5
 
< 0.1%
-117.078955 22
 
< 0.1%
-117.08094 63
 
< 0.1%
-117.0888411 3
 
< 0.1%
-117.09883 228
0.1%
-117.1284737 3
 
< 0.1%
-117.1286861 55
 
< 0.1%
-117.1403337 5
 
< 0.1%

Electric Utility
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
69049 
PUGET SOUND ENERGY INC
37798 
CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
32898 
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)
10753 
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY
8215 
Other values (70)
27763 

Length

Max length112
Median length110
Mean length44.336633
Min length10

Characters and Unicode

Total characters8267718
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
2nd rowPUGET SOUND ENERGY INC
3rd rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
4th rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
5th rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)

Common Values

ValueCountFrequency (%)
PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA) 69049
37.0%
PUGET SOUND ENERGY INC 37798
20.3%
CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA) 32898
17.6%
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA) 10753
 
5.8%
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY 8215
 
4.4%
PUGET SOUND ENERGY INC||PUD NO 1 OF WHATCOM COUNTY 4205
 
2.3%
BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||INLAND POWER & LIGHT COMPANY 2959
 
1.6%
BONNEVILLE POWER ADMINISTRATION||PUD 1 OF SNOHOMISH COUNTY 1607
 
0.9%
PACIFICORP 1388
 
0.7%
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF BENTON COUNTY 1331
 
0.7%
Other values (65) 16273
 
8.7%

Length

2024-06-05T18:03:08.542449image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 175255
12.6%
164237
11.8%
wa 114753
 
8.2%
tacoma 113233
 
8.1%
energy 112061
 
8.0%
sound 112061
 
8.0%
puget 111052
 
8.0%
inc||city 69049
 
4.9%
power 41047
 
2.9%
inc 38079
 
2.7%
Other values (111) 344734
24.7%

Most occurring characters

ValueCountFrequency (%)
1209085
14.6%
O 607663
 
7.3%
N 589137
 
7.1%
T 572494
 
6.9%
A 557667
 
6.7%
E 540755
 
6.5%
I 451755
 
5.5%
C 450421
 
5.4%
Y 302166
 
3.7%
U 290252
 
3.5%
Other values (26) 2696323
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8267718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1209085
14.6%
O 607663
 
7.3%
N 589137
 
7.1%
T 572494
 
6.9%
A 557667
 
6.7%
E 540755
 
6.5%
I 451755
 
5.5%
C 450421
 
5.4%
Y 302166
 
3.7%
U 290252
 
3.5%
Other values (26) 2696323
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8267718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1209085
14.6%
O 607663
 
7.3%
N 589137
 
7.1%
T 572494
 
6.9%
A 557667
 
6.7%
E 540755
 
6.5%
I 451755
 
5.5%
C 450421
 
5.4%
Y 302166
 
3.7%
U 290252
 
3.5%
Other values (26) 2696323
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8267718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1209085
14.6%
O 607663
 
7.3%
N 589137
 
7.1%
T 572494
 
6.9%
A 557667
 
6.7%
E 540755
 
6.5%
I 451755
 
5.5%
C 450421
 
5.4%
Y 302166
 
3.7%
U 290252
 
3.5%
Other values (26) 2696323
32.6%

2020 Census Tract
Categorical

HIGH CARDINALITY 

Distinct1768
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
53033028200.0
 
2569
53033026200.0
 
1002
53033032321.0
 
850
53033028500.0
 
756
53033009300.0
 
735
Other values (1763)
180564 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters2424188
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row53033003601.0
2nd row53035080700.0
3rd row53033031708.0
4th row53033024002.0
5th row53033023601.0

Common Values

ValueCountFrequency (%)
53033028200.0 2569
 
1.4%
53033026200.0 1002
 
0.5%
53033032321.0 850
 
0.5%
53033028500.0 756
 
0.4%
53033009300.0 735
 
0.4%
53067011200.0 716
 
0.4%
53033032318.0 617
 
0.3%
53061052107.0 606
 
0.3%
53033032220.0 597
 
0.3%
53033025006.0 596
 
0.3%
Other values (1758) 177432
95.2%

Length

2024-06-05T18:03:08.714042image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
53033028200.0 2569
 
1.4%
53033026200.0 1002
 
0.5%
53033032321.0 850
 
0.5%
53033028500.0 756
 
0.4%
53033009300.0 735
 
0.4%
53067011200.0 716
 
0.4%
53033032318.0 617
 
0.3%
53061052107.0 606
 
0.3%
53033032220.0 597
 
0.3%
53033025006.0 596
 
0.3%
Other values (1758) 177432
95.2%

Most occurring characters

ValueCountFrequency (%)
0 854122
35.2%
3 499036
20.6%
5 272283
 
11.2%
. 186476
 
7.7%
1 173950
 
7.2%
2 162419
 
6.7%
6 72136
 
3.0%
7 62162
 
2.6%
4 58362
 
2.4%
9 52387
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2424188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 854122
35.2%
3 499036
20.6%
5 272283
 
11.2%
. 186476
 
7.7%
1 173950
 
7.2%
2 162419
 
6.7%
6 72136
 
3.0%
7 62162
 
2.6%
4 58362
 
2.4%
9 52387
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2424188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 854122
35.2%
3 499036
20.6%
5 272283
 
11.2%
. 186476
 
7.7%
1 173950
 
7.2%
2 162419
 
6.7%
6 72136
 
3.0%
7 62162
 
2.6%
4 58362
 
2.4%
9 52387
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2424188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 854122
35.2%
3 499036
20.6%
5 272283
 
11.2%
. 186476
 
7.7%
1 173950
 
7.2%
2 162419
 
6.7%
6 72136
 
3.0%
7 62162
 
2.6%
4 58362
 
2.4%
9 52387
 
2.2%

Interactions

2024-06-05T18:02:58.069376image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:52.971730image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:54.468074image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:55.366144image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:56.259667image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:57.205567image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:58.233714image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:53.151347image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:54.612691image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:55.529595image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:56.447686image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:57.354412image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:58.395156image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:53.376384image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:54.743162image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:55.663481image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:56.598642image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:57.495943image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:58.557476image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:53.929239image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:54.905141image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:55.797006image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:56.762228image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:57.640949image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:58.709950image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:54.086084image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:55.042082image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:55.923064image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:56.916313image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:57.777544image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:58.870502image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:54.264065image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:55.219118image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:56.071844image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:57.064446image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T18:02:57.922062image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Missing values

2024-06-05T18:02:59.054509image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-05T18:02:59.624993image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-05T18:03:00.713771image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeLegislative DistrictDOL Vehicle IDVehicle LocationLatitudeLongitudeElectric Utility2020 Census Tract
0WBY8P6C58KKingSeattleWA98115.02019BMWI3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible15343.0259254397POINT (-122.3008235 47.6862671)47.686267-122.300824CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303300e+10
15YJSA1DN4DKitsapBremertonWA98312.02013TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible20835.0127420940POINT (-122.6961203 47.5759584)47.575958-122.696120PUGET SOUND ENERGY INC5.303508e+10
25YJSA1E26JKingKentWA98042.02018TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible24947.0170287183POINT (-122.1145138 47.3581107)47.358111-122.114514PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
3WBY2Z2C54EKingBellevueWA98004.02014BMWI8Plug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range1441.0205545868POINT (-122.202397 47.619252)47.619252-122.202397PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
45YJXCDE23JKingBellevueWA98004.02018TESLAMODEL XBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible23841.0237977386POINT (-122.202397 47.619252)47.619252-122.202397PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
5WBY33AW0XPKingSeattleWA98109.02023BMWI4Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched036.0238283545POINT (-122.3441532 47.6305366)47.630537-122.344153CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303301e+10
65YJ3E1EB5LKingBothellWA98011.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible3221.0123837269POINT (-122.201408 47.754528)47.754528-122.201408PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
71V2GNPE86PKingSammamishWA98075.02023VOLKSWAGENID.4Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched041.0266068799POINT (-122.0181135 47.5880568)47.588057-122.018113PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
8WVWPP7AU0GKingBellevueWA98004.02016VOLKSWAGENE-GOLFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible8348.0156800388POINT (-122.202397 47.619252)47.619252-122.202397PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
93C3CFFGE8DKingBellevueWA98004.02013FIAT500Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible8741.0267527928POINT (-122.202397 47.619252)47.619252-122.202397PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeLegislative DistrictDOL Vehicle IDVehicle LocationLatitudeLongitudeElectric Utility2020 Census Tract
186869KM8KN4AE4PGrantQuincyWA98848.02023HYUNDAIIONIQ 5Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched013.0230075947POINT (-119.8619179 47.2248994)47.224899-119.861918PUD NO 2 OF GRANT COUNTY5.302501e+10
1868705YJSA1E55PPierceGig HarborWA98335.02023TESLAMODEL SBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched026.0245475464POINT (-122.6046494 47.3087246)47.308725-122.604649BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY5.305307e+10
1868717SAYGDEF1PKingEnumclawWA98022.02023TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched031.0233676383POINT (-121.98953 47.20347)47.203470-121.989530PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
1868727SAYGDEF3PSnohomishSnohomishWA98296.02023TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched044.0228055278POINT (-122.121841 47.841036)47.841036-122.121841PUGET SOUND ENERGY INC5.306105e+10
1868737SAYGDEE0NOkanoganWinthropWA98862.02022TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched012.0203695124POINT (-120.2222424 48.4862039)48.486204-120.222242OKANOGAN COUNTY ELEC COOP, INC5.304797e+10
186874JTMEB3FVXMSnohomishArlingtonWA98223.02021TOYOTARAV4 PRIMEPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible4210.0167257387POINT (-122.11597 48.194109)48.194109-122.115970BONNEVILLE POWER ADMINISTRATION||PUD 1 OF SNOHOMISH COUNTY5.306105e+10
1868757SAYGAEEXPSnohomishStanwoodWA98292.02023TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched010.0255471611POINT (-122.326873 48.2144825)48.214483-122.326873BONNEVILLE POWER ADMINISTRATION||PUD 1 OF SNOHOMISH COUNTY5.306105e+10
1868763FA6P0SUXKKingAuburnWA98001.02019FORDFUSIONPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range2630.0326904777POINT (-122.2651204 47.3164638)47.316464-122.265120PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
1868777SAYGDEE1PPierceTacomaWA98422.02023TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched027.0252396427POINT (-122.389973 47.291035)47.291035-122.389973BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY5.305394e+10
1868787SAYGDEE1RDouglasEast WenatcheeWA98802.02024TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched012.0261058612POINT (-120.2609057 47.4167133)47.416713-120.260906PUD NO 1 OF DOUGLAS COUNTY5.301795e+10